In this paper we discuss the application of Artificial Intelligence (AI) to the exemplary industrial use case of the two-dimensional commissioning problem in a high-bay storage, which essentially can be phrased as an instance of Traveling Salesperson Problem (TSP). We investigate the mlrose library that provides an TSP optimizer based on various heuristic optimization techniques. Our focus is on two methods, namely Genetic Algorithm (GA) and Hill Climbing (HC), which are provided by mlrose. We present improvements for both methods that yield shorter tour lengths, by moderately exploiting the problem structure of TSP. That is, the proposed improvements have a generic character and are not limited to TSP only.

翻译：本文探讨了人工智能（AI）在高架仓库二维拣选问题这一典型工业用例中的应用，该问题本质上可表述为旅行商问题（TSP）的一个实例。我们研究了基于多种启发式优化技术提供TSP优化器的mlrose库。重点聚焦于该库提供的两种方法：遗传算法（GA）和爬山算法（HC）。通过适度利用TSP的问题结构特征，我们针对这两种方法提出了改进方案，从而获得更短的路径长度。这些改进具有通用性，不仅限于TSP场景。